In a recent video interview, the IBM CEO Ginni Rometty comments that Watson 2.0 will understand images that it sees, and that Watson 3.0 will be able to debate, i.e. to understand what it is talking about with another party. An impressive roadmap, each of these is an incredible leap forward from its predecessor.

It is, however, worth qualifying the term 'understand'. It is being used figuratively, not literally, to communicate the rough order of magnitude improvement in capability. When such a leap is made, it seems analogous to sentient understanding, even though it isn't. Imagine for a moment what Archimedes would have thought at first of a hand-held calculator, given that he had the power of Roman numerals with which to calculate pi to several digits. And yet, we would not now interpret such a device as artificial intelligence. As soon as the mechanical nature of a level of capability becomes clear, so too does the fact that it does not constitute sentient intelligence (Hofstadter's exposition of Tesler's "theorem").

You can see this assertion play out in multiple levels of Bob Sutor's scale of cognitive computing. There are levels that are clearly not cognitive intelligence, as Sutor points out, but if you lay out the scale on a timeline of decades or centuries, it is clear that each level might once have been interpreted as being indistinguishable from magic.

So where on Sutor's scale is Watson? And what implications does that have for development best practices?

Watson is clearly not on the "Sentient (we can do without humans) systems" level. As sentient beings, we don't just know things with a certain calculated accuracy or confidence level, or determine that we don't know if our confidence is low. We experience desire to know more, and we experience fear of the unknown. We are teetering bulbs of dread and dream (Hofstadter's delightful invocation of a Russell Edson poem). I urge you to let that characterization of us sink into your mind. In Watson technology, IBM has modeled a certain class of knowledge and mechanical reasoning, and in other research, IBM is doing so by simulating some of the known structure of biological brains. However, we don't yet know how to model fear and desire, dread and dream. In my opinion, these are inextricably bound together in sentient intelligence, separating it from simulated intelligence. In other words, intelligent behavior is a construct that works for the dread and dream engine of the sentient, and in the absence of dread and dream, seeming intelligent behavior is but a mechanical simulation of understanding. As an aside, I hope we only manage to model desire and fear around the same time we figure out how to model ethics (as Asimov cautions).

Does this characterization of Watson as a mechanical simulation of understanding detract from its value? Does it detract from the order of magnitude improvement it heralds as an usher of the era of cognitive computing? Of course not, quite the opposite. It is simply fantastic that this level of "Learning, Reasoning, Inference Systems" (Sutor's scale) is now computationally and economically feasible at the scale needed to help sentient intelligence (that's us) to solve real world problems. Quick, what is the square root of 7. Can't do it? No problem. Even if you're Arthur Benjamin, you'd be better off just hitting a few keys on a calculator. Quick, what are the most likely diagnoses for the patient's presenting symptoms? An "expert advisor" like Watson can be just what it takes to help determine the next best action, especially when time is of the essence because a life hangs in the balance.

The term "expert advisor" is appropriate. It conveys that the system is a "Learning, Reasoning, Inference System" that does not have sentient understanding and is therefore made available to advise and guide the actions of an expert. This is analogous to the way spreadsheets guide the results reported by accountants and chief financial officers. That being said, we also know not to put spreadsheets in the hands of toddlers. From a development practice standpoint, it is crucial to keep in mind that "expert advisor" means that the deployed system should be advising someone who is a qualified expert in the exact domain in which the "expert advisor" system was trained. Especially when a life hangs in the balance, access to the "expert advisor" system needs to be performed by those with expert qualifications in the domain because only they can reasonably be expected to use sentient understanding to interpret and follow up on the advice. In other words, the term 'expert' in 'expert advisor' should apply to the user more so than the advisor.

Now, given an enterprise workforce of those with qualified sentient understanding of their topic areas, Watson-style expert advisors are just the type of technological advancement that will help them work smarter, not harder, to meet the needs of customers and colleagues and to produce a competitive advantage for the business.

Due to being an eponymous blog, it has become that time to redirect my blog and increase its aperture to cover a much wider range of IBM-related topics that developers will find interesting and that reflect my own broader range of pursuits and thoughts within IBM.

These days I work in the Smarter Workforce segment of IBM Collaboration Solutions, which is responsible for building out cloud-based solutions for employee talent optimization. How do you attract employees? Retain them? Provide education when they are recruited, promoted or need remediation? How do you best equip employees to share information and enable one another to achieve better customer satisfaction and better business results? How do you measure the results?

So, if you're not in this particular problem space, why should you care? Well, there is a remarkable dynamism in this problem space due to the fact that it seeks to help human beings interact more effectively and efficiently with other human beings. As a result, many of today's most interesting topics, technologies and techniques are applicable: social computing, cloud computing, mobile computing, security, bigdata, business analytics and algorithms, and even psychological science and cognitive computing.

Think about what it takes to give everyone a smarter edge. Think of everything that might be needed to do it, plus everything they might want to do, and everything they might want to do it with. Then, think of enabling them to do it everywhere. Now we're talking the same language.